Gastos Casa
20 marzo, 2023
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 22/2/2023 | Uber | 6414 | Tami | NA |
| 22/2/2023 | Comida | 52690 | Tami | Restaurant Valpo |
| 22/2/2023 | Uber | 5215 | Tami | NA |
| 24/2/2023 | Uber | 8458 | Tami | NA |
| 24/2/2023 | Comida | 7300 | Tami | Helados Reñaca |
| 25/2/2023 | Uber | 2889 | Tami | NA |
| 26/2/2023 | Uber | 6876 | Tami | NA |
| 26/2/2023 | Enceres | 7500 | Andrés | Merval |
| 26/2/2023 | Comida | 68970 | Andrés | Il papparazzo |
| 26/2/2023 | Electricidad | 40440 | Andrés | Enel |
| 26/2/2023 | Comida | 18480 | Tami | Café Turri Valpo |
| 26/2/2023 | Uber | 9602 | Tami | NA |
| 27/2/2023 | Comida | 9090 | Tami | Copec |
| 27/2/2023 | Bencina + tag | 40000 | Tami | NA |
| 27/2/2023 | Comida | 62535 | Tami | NA |
| 1/3/2023 | Uber | 2960 | Andrés | uber jueves reñaca-viña tarde |
| 1/3/2023 | Uber | 3433 | Andrés | uber martes tarde reñaca |
| 1/3/2023 | Uber | 3308 | Andrés | viaje lunes noche |
| 1/3/2023 | Comida | 7200 | Andrés | Frutos secos |
| 5/3/2023 | Comida | 62296 | Tami | NA |
| 6/3/2023 | Enceres | 110000 | Andrés | arreglo reja |
| 9/3/2023 | Forro cortina ducha | 2490 | Tami | NA |
| 4/3/2023 | Microondas regalo | 40000 | Tami | NA |
| 9/3/2023 | Comida | 106490 | Tami | Soul Bar |
| 9/3/2023 | Comida | 27642 | Tami | NA |
| 13/3/2023 | Comida | 51473 | Tami | NA |
| 13/3/2023 | Diosi | 20990 | Tami | Antiparasitario |
| 16/3/2023 | Vacunas Influenza | 19980 | Tami | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 5.8551e+08 2 6.1203 0.0023 **
## lag_depvar 8.2562e+10 1 1726.0362 <2e-16 ***
## Residuals 2.6691e+10 558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 985.8502 13471.83 0.018385
## 2-0 27878.558 22165.4963 33591.62 0.000000
## 2-1 20649.720 17236.1307 24063.31 0.000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 406 50112.82 15540.714
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2000.614782 4032.542491 -530.265070 2444.839364 -2954.194259
## 7 8 9 10 11
## 524.357145 -5647.598365 -1197.012269 -3976.297969 -437.821547
## 12 13 14 15 16
## -4956.724478 -1638.336675 -928.476355 351.205345 -3262.917503
## 17 18 19 20 21
## -404.051812 -2152.449918 6579.521625 -1528.129424 -1210.590497
## 22 23 24 25 26
## 1471.407162 -1183.937122 234.852722 1697.599524 -7092.694883
## 27 28 29 30 31
## 934.886545 8186.142189 440.672868 8.779585 -2378.928186
## 32 33 34 35 36
## 1589.270220 4590.745479 1159.243753 2425.110690 -1829.130706
## 37 38 39 40 41
## 4637.765548 4321.355849 -2246.144896 -2962.676174 -1103.081567
## 42 43 44 45 46
## -10738.658010 7257.727615 2552.949656 1371.363008 8113.609752
## 47 48 49 50 51
## 719.499624 6560.248721 6762.871739 -5818.416366 -4758.137843
## 52 53 54 55 56
## -5042.228507 -7929.214302 6104.166517 -4079.369987 -4909.575396
## 57 58 59 60 61
## 3827.032976 875.078556 -40.313945 135.084293 -5002.092219
## 62 63 64 65 66
## 18106.065610 3680.394702 -3599.623871 5954.584340 7388.668978
## 67 68 69 70 71
## 14701.528297 1794.852221 -13118.190307 -1265.227759 4675.395030
## 72 73 74 75 76
## -4857.403939 -4382.357958 -10491.754008 2438.976945 -5415.931528
## 77 78 79 80 81
## 1032.908100 -6889.555366 505.204599 -2389.001233 -2731.078542
## 82 83 84 85 86
## -3972.585968 -585.176987 2271.556383 3732.552605 462.555212
## 87 88 89 90 91
## -495.475293 185.648209 4293.092786 -1157.084803 1152.503538
## 92 93 94 95 96
## -2059.265751 -1046.093286 172.875830 271.376047 -7486.004972
## 97 98 99 100 101
## 2366.768598 -8616.598146 -2979.513253 -4082.280465 -1786.332469
## 102 103 104 105 106
## -1309.670646 3135.181183 -2371.830221 2560.920521 -1179.009488
## 107 108 109 110 111
## 949.040533 2571.602128 -3159.663099 -4737.328595 -877.266215
## 112 113 114 115 116
## 1877.507337 11676.976575 -1220.353162 2684.258290 4285.136178
## 117 118 119 120 121
## 3535.722403 -1059.867265 -4684.501778 -3710.771179 2320.036081
## 122 123 124 125 126
## -1724.907726 1342.027602 8864.088445 880.157677 162.227805
## 127 128 129 130 131
## -2492.844420 2672.260775 7076.092310 1055.337676 -8458.521129
## 132 133 134 135 136
## 1758.550530 4149.362535 -3138.711126 -1407.364722 -847.398338
## 137 138 139 140 141
## -3876.711614 1174.019840 -499.445620 -2918.406967 1705.073023
## 142 143 144 145 146
## -1886.945491 -7840.107562 2005.752560 -3502.609041 2071.512073
## 147 148 149 150 151
## -277.613821 1004.754561 -371.947358 1340.150014 1180.460070
## 152 153 154 155 156
## 3355.021346 -4852.528655 -1181.407213 -3245.375639 5938.420255
## 157 158 159 160 161
## 9749.406543 -3282.815297 -4635.006114 3738.187367 346.957014
## 162 163 164 165 166
## 2853.337530 -5741.545780 -6594.481047 4293.226788 17547.518102
## 167 168 169 170 171
## 3831.594384 -188.638257 -2241.699823 -910.335305 3778.833218
## 172 173 174 175 176
## -31.936906 -7882.694383 3031.808767 4501.940579 813.138188
## 177 178 179 180 181
## 8937.638693 -9039.872546 -3292.904255 -10576.177480 -11100.425351
## 182 183 184 185 186
## 1348.210139 9417.283783 -1275.437129 6081.031872 6724.415531
## 187 188 189 190 191
## 13342.038898 8642.198169 -3841.018664 2664.835362 10565.613927
## 192 193 194 195 196
## -1430.561924 -2246.473314 -10096.984141 -6210.314219 1370.199666
## 197 198 199 200 201
## -5092.234868 -9666.104297 5493.717327 -2938.885030 -1586.584672
## 202 203 204 205 206
## -678.179632 6622.347987 10026.554976 742.790005 3086.385816
## 207 208 209 210 211
## 3262.723953 5952.274179 13010.229856 -5487.928527 -11117.264508
## 212 213 214 215 216
## -5516.885185 -10450.275777 -4959.023489 1637.458638 -12890.027417
## 217 218 219 220 221
## 16486.716159 7949.739011 1685.641783 26846.872364 12736.607909
## 222 223 224 225 226
## 7559.587007 14252.999230 -3671.902100 -1522.946013 3980.161025
## 227 228 229 230 231
## 561.985234 2941.051731 9199.349129 6039.752749 -1689.853806
## 232 233 234 235 236
## -1623.619973 9619.425216 -11300.046289 -7108.114607 -8385.304683
## 237 238 239 240 241
## -9965.062461 3194.126994 1480.404771 -8162.336089 -8869.885718
## 242 243 244 245 246
## 9200.161447 -7633.923960 2605.394678 -10172.970287 -3943.928123
## 247 248 249 250 251
## 1530.204043 1119.612254 -12192.629081 3744.922600 2178.126250
## 252 253 254 255 256
## 4335.882562 2270.051198 -1020.105406 11277.348096 21039.996937
## 257 258 259 260 261
## 3389.110849 -4083.982868 4279.453232 -1523.607346 3898.496258
## 262 263 264 265 266
## -4688.562355 -10744.521488 -4602.288328 -403.106145 -5068.534254
## 267 268 269 270 271
## 8890.112092 -4151.109733 4310.306179 -1978.675296 4554.505287
## 272 273 274 275 276
## 839.488221 7432.738662 -1272.879239 12157.896822 -4437.418775
## 277 278 279 280 281
## 1858.073934 -241.118945 7978.337054 -4922.353648 -2607.054540
## 282 283 284 285 286
## -11142.021266 -2564.253438 18759.831306 7915.152108 2872.104985
## 287 288 289 290 291
## -492.136832 1036.995809 6526.604979 7015.045113 -18635.945823
## 292 293 294 295 296
## -11025.905903 -8016.471528 9767.787165 3191.608705 -1053.761027
## 297 298 299 300 301
## 27527.497529 10217.206268 5055.785637 9670.339300 3011.019505
## 302 303 304 305 306
## -882.519210 8039.336234 -24150.009419 -3411.963559 -52.035500
## 307 308 309 310 311
## -6841.441451 -3846.597175 3059.576892 -9057.557494 -3097.267460
## 312 313 314 315 316
## -8049.162161 1703.588212 -3005.578125 2196.371028 -3929.705984
## 317 318 319 320 321
## 27597.624231 -574.691612 3435.572561 10973.070614 5734.253786
## 322 323 324 325 326
## 32524.530982 5269.975661 -20781.051931 1932.708910 1250.456893
## 327 328 329 330 331
## -6325.439210 -1598.049191 -33130.931761 1042.653307 -2129.582119
## 332 333 334 335 336
## 88.057027 -2978.784945 4279.917668 -236.760380 -6749.098801
## 337 338 339 340 341
## -2911.650420 -1983.662627 -7468.482286 4064.390738 -1155.371508
## 342 343 344 345 346
## -1520.731697 -775.756412 395.755825 701.432254 -1398.837857
## 347 348 349 350 351
## -9227.888124 -12993.228076 2526.135218 -4104.775332 -3438.822033
## 352 353 354 355 356
## -5758.796973 1973.051499 1607.292978 2975.084347 -3546.971517
## 357 358 359 360 361
## -299.218494 892.642710 7226.938597 486.317781 168.399365
## 362 363 364 365 366
## 2787.111542 -2548.807246 -676.367036 -8542.169132 -4420.562529
## 367 368 369 370 371
## -6002.987162 -4735.386635 -7034.235404 5237.864691 591.927104
## 372 373 374 375 376
## 7339.493539 -7421.111806 -2045.423863 -3168.837991 -2245.807935
## 377 378 379 380 381
## -12234.405595 2131.619522 -10405.520669 5929.115011 9565.643640
## 382 383 384 385 386
## 3348.095890 -2182.207743 1818.748429 6954.175270 11613.025722
## 387 388 389 390 391
## -5612.716136 -5178.879950 24.175767 8742.440450 1986.224588
## 392 393 394 395 396
## 11388.135842 -9723.522600 2928.783317 863.465307 711.056452
## 397 398 399 400 401
## -506.640414 -416.216973 -14340.130828 8686.418159 -1015.383068
## 402 403 404 405 406
## -1202.507871 7155.876700 -7761.590514 -1117.064165 -2346.044233
## 407 408 409 410 411
## -5626.770814 -2656.732688 -3707.697301 -8538.760066 6360.337222
## 412 413 414 415 416
## 1863.294509 -7152.474950 -7465.070322 14455.338884 4033.732627
## 417 418 419 420 421
## 4699.650878 -7837.923534 -4540.688851 -2391.721748 3033.690787
## 422 423 424 425 426
## -13798.633488 -2565.564997 -8868.083559 3253.524650 7215.220976
## 427 428 429 430 431
## 6804.804076 -3767.698150 -3899.418220 -4497.673461 -1560.888836
## 432 433 434 435 436
## -5481.584979 -6391.603402 -5709.015730 -1147.792060 -601.600089
## 437 438 439 440 441
## -4729.234348 2830.355999 5084.060688 -4819.089063 -1918.606113
## 442 443 444 445 446
## 1816.153326 -3600.469693 3073.712594 -6344.081419 -11872.619504
## 447 448 449 450 451
## -4264.977558 9895.055460 -1789.442266 4997.410427 -5631.089433
## 452 453 454 455 456
## -881.147941 625.475171 3266.815546 -12031.142355 3613.054839
## 457 458 459 460 461
## -6459.877407 6766.974903 3252.269081 2743.687720 -3612.344631
## 462 463 464 465 466
## 2327.186029 224.733513 2023.855787 -292.641971 3578.121945
## 467 468 469 470 471
## -2414.632491 6030.968111 -6719.786438 -2736.639230 -1973.907832
## 472 473 474 475 476
## -4429.082758 3235.588057 8035.675021 -5783.393116 1721.386161
## 477 478 479 480 481
## -5942.024771 -2603.526152 2255.345288 -12686.957495 -9506.304906
## 482 483 484 485 486
## -948.790184 275.102929 -707.088457 -1086.501137 -9329.221632
## 487 488 489 490 491
## 11353.881635 6488.958185 7669.701487 -5192.659977 5611.527216
## 492 493 494 495 496
## 9533.193265 6287.628740 -13245.100027 -10332.538358 -3202.038230
## 497 498 499 500 501
## -863.436515 -279.872886 -7380.029319 861.599314 4539.613128
## 502 503 504 505 506
## 5759.633013 908.926682 328.134873 -6992.068016 819.516402
## 507 508 509 510 511
## -4798.531378 2084.511183 -1044.692929 -7905.615087 -346.979984
## 512 513 514 515 516
## -2418.668614 -330.786050 1589.470777 -9238.536671 -7506.109615
## 517 518 519 520 521
## 24547.869995 10078.246883 6125.986519 -5094.633625 3038.156873
## 522 523 524 525 526
## 17255.749480 11701.209002 -23929.481698 -4834.027503 -3505.073316
## 527 528 529 530 531
## 4802.818921 -127.029995 -10872.687138 4626.308713 14144.331660
## 532 533 534 535 536
## -4747.322731 4603.510815 5782.091537 -1566.818264 -4317.742449
## 537 538 539 540 541
## -6848.992913 -1871.308395 8554.701151 357.762785 -7910.615086
## 542 543 544 545 546
## 2050.640570 -364.159642 602.893355 -10793.530449 -10819.338439
## 547 548 549 550 551
## 2288.690349 7252.764559 -1069.750117 1085.477342 -7472.259908
## 552 553 554 555 556
## 8816.205660 1155.251538 -11696.853010 9412.856404 8904.211383
## 557 558 559 560 561
## 340.119269 5091.673204 -3339.845495 14342.227139 21724.886527
## 562 563
## -6252.728827 -9468.279834
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17268.67 20106.46 24346.41 24065.30 26410.91 23752.36 24466.31 19714.16
## 10 11 12 13 14 15 16 17
## 19451.58 16803.11 17578.01 14318.19 14369.19 15031.65 16722.63 15048.19
## 18 19 20 21 22 23 24 25
## 16079.45 15455.05 22514.13 21601.16 21082.74 22966.51 22294.72 22945.11
## 26 27 28 29 30 31 32 33
## 24784.98 18733.40 20453.86 28265.33 28322.79 27996.79 25634.02 27031.83
## 34 35 36 37 38 39 40 41
## 30862.18 31209.46 32613.99 30132.81 34121.64 37319.14 34384.96 31206.37
## 42 43 44 45 46 47 48 49
## 30057.94 20668.56 28162.48 30590.92 31676.53 38492.07 37988.32 42635.13
## 50 51 52 53 54 55 56 57
## 46857.42 39579.42 34165.80 29204.93 22371.98 28641.23 25233.15 21542.97
## 58 59 60 61 62 63 64 65
## 25936.78 27192.17 27488.20 27898.66 23783.22 40319.75 42157.62 37419.27
## 66 67 68 69 70 71 72 73
## 41612.33 46511.76 57144.72 55165.05 40456.94 37971.03 40978.98 35297.93
## 74 75 76 77 78 79 80 81
## 30765.18 21499.31 24690.22 20629.38 22708.56 17620.94 19629.72 18858.79
## 82 83 84 85 86 87 88 89
## 17889.73 15965.03 17238.59 20834.73 25237.87 26224.48 26249.35 26864.05
## 90 91 92 93 94 95 96 97
## 30975.51 29809.93 30805.98 28876.81 28079.27 28446.20 28851.43 22450.09
## 98 99 100 101 102 103 104 105
## 25455.17 18508.66 17368.57 15415.76 15714.53 16389.68 20847.54 19934.08
## 106 107 108 109 110 111 112 113
## 23433.58 23224.25 24894.83 27762.09 25268.47 21723.69 21998.21 24635.74
## 114 115 116 117 118 119 120 121
## 35464.35 33663.17 35494.58 38482.99 40432.44 38128.50 32966.63 29320.11
## 122 123 124 125 126 127 128 129
## 31396.05 29681.69 30859.34 38433.99 38077.63 37142.27 34016.17 35791.48
## 130 131 132 133 134 135 136 137
## 41171.52 40613.66 31844.45 33105.07 36284.28 32706.79 31099.40 30187.43
## 138 139 140 141 142 143 144 145
## 26755.84 28165.59 27935.98 25629.93 27647.66 26276.96 19900.25 22920.75
## 146 147 148 149 150 151 152 153
## 20754.63 23721.90 24260.10 25845.23 26026.71 27675.40 28971.84 31993.96
## 154 155 156 157 158 159 160 161
## 27479.12 26744.52 24307.87 30182.45 41303.24 39639.01 37012.67 42016.33
## 162 163 164 165 166 167 168 169
## 43420.23 46824.83 42305.77 37628.49 43035.77 59283.98 61488.78 59908.13
## 170 171 172 173 174 175 176 177
## 56744.34 55148.88 57842.51 56869.84 49187.48 52001.63 55731.86 55767.93
## 178 179 180 181 182 183 184 185
## 62873.16 53406.90 50168.61 41007.71 32575.08 36071.72 46141.72 45599.54
## 186 187 188 189 190 191 192 193
## 51532.58 57258.53 68005.80 73271.16 66986.74 67179.53 74226.42 69917.19
## 194 195 196 197 198 199 200 201
## 65454.84 54734.31 48784.23 50203.81 45813.10 38007.85 44411.31 42644.58
## 202 203 204 205 206 207 208 209
## 42283.75 42760.51 49532.02 58391.78 58022.61 59741.70 61392.01 65170.63
## 210 211 212 213 214 215 216 217
## 74605.79 66714.84 54943.03 49569.70 40595.88 37563.68 40667.03 30720.28
## 218 219 220 221 222 223 224 225
## 47637.55 54934.07 55832.98 78522.96 85993.13 87989.72 95555.90 86536.80
## 226 227 228 229 230 231 232 233
## 80555.12 80138.44 76799.52 75963.79 80685.10 82044.85 76498.76 71727.57
## 234 235 236 237 238 239 240 241
## 77362.47 64054.54 56117.45 48094.78 39734.16 43912.17 46057.76 39530.17
## 242 243 244 245 246 247 248 249
## 33230.70 43479.07 37745.03 41667.68 33957.21 32667.37 36310.53 39125.06
## 250 251 252 253 254 255 256 257
## 29984.93 35903.30 39692.12 44869.66 47578.96 47073.22 57340.00 74779.17
## 258 259 260 261 262 263 264 265
## 74594.84 67927.69 69404.61 65637.93 67079.28 60857.66 50167.86 46208.39
## 266 267 268 269 270 271 272 273
## 46417.11 42536.75 51311.68 47597.12 51730.10 49852.92 53906.80 54201.83
## 274 275 276 277 278 279 280 281
## 60199.31 57841.39 67482.28 61427.21 61636.55 59991.09 65714.93 59466.20
## 282 283 284 285 286 287 288 289
## 56041.45 45628.40 44030.45 61205.56 66717.32 67125.42 64551.58 63641.97
## 290 291 292 293 294 295 296 297
## 67629.67 71526.95 52586.48 42721.33 36752.21 47039.39 50270.48 49387.36
## 298 299 300 301 302 303 304 305
## 73503.51 79429.21 80094.66 84691.84 82896.38 77943.09 81398.44 56380.39
## 306 307 308 309 310 311 312 313
## 52653.89 52334.73 46145.45 43364.14 46955.56 39532.41 38258.73 32838.27
## 314 315 316 317 318 319 320 321
## 36610.29 35794.34 39613.13 37604.23 63305.26 61153.57 62771.79 70743.46
## 322 323 324 325 326 327 328 329
## 73122.90 98520.31 96903.34 72813.43 71615.26 69978.01 61956.33 59088.07
## 330 331 332 333 334 335 336 337
## 29135.78 32811.15 33249.23 35561.50 34904.51 40652.47 41724.53 36987.79
## 338 339 340 341 342 343 344 345
## 36204.81 36331.05 31665.47 37644.66 38305.87 38563.47 39436.39 41216.42
## 346 347 348 349 350 351 352 353
## 43032.41 42784.89 35752.80 26351.72 31678.78 30543.54 30134.94 27759.23
## 354 355 356 357 358 359 360 361
## 32422.71 36164.63 40613.54 38808.50 40064.64 42196.06 49566.97 50115.74
## 362 363 364 365 366 367 368 369
## 50316.75 52771.81 50263.51 49709.88 42379.28 39585.27 35774.82 33560.81
## 370 371 372 373 374 375 376 377
## 29631.56 36895.50 39174.94 47034.54 41026.00 40474.98 39017.09 38551.41
## 378 379 380 381 382 383 384 385
## 29449.09 34032.09 27106.60 35298.93 45598.05 49151.78 47430.82 49415.97
## 386 387 388 389 390 391 392 393
## 55615.69 65070.00 58303.59 52789.97 52519.56 59874.92 60396.58 69036.81
## 394 395 396 397 398 399 400 401
## 58178.22 59739.96 59301.51 58787.07 57278.93 56044.56 42846.58 51404.10
## 402 403 404 405 406 407 408 409
## 50407.79 49377.41 55757.73 48324.64 47638.04 45970.20 41661.59 40496.13
## 410 411 412 413 414 415 416 417
## 38566.33 32679.81 40526.85 43443.62 38133.36 33237.66 48060.70 51892.92
## 418 419 420 421 422 423 424 425
## 55809.35 48303.12 44638.44 43318.74 46893.49 35350.42 35080.51 29358.05
## 426 427 428 429 430 431 432 433
## 34929.64 43230.05 50099.70 46875.70 43953.96 40889.17 40777.73 37267.03
## 434 435 436 437 438 439 440 441
## 33418.02 30661.08 32232.03 34075.38 32086.50 36936.80 43122.09 39885.03
## 442 443 444 445 446 447 448 449
## 39591.99 42588.61 40481.57 44458.08 39720.48 30781.98 29623.23 40943.16
## 450 451 452 453 454 455 456 457
## 40625.73 46258.52 41908.86 42257.38 43872.61 47578.71 37485.95 42319.45
## 458 459 460 461 462 463 464 465
## 37757.60 45302.02 48810.60 51422.63 48162.81 50495.98 50696.86 52438.21
## 466 467 468 469 470 471 472 473
## 51937.45 54871.63 52208.60 57243.36 50525.21 48143.91 46734.65 43369.98
## 474 475 476 477 478 479 480 481
## 47113.90 54552.96 48998.04 50695.74 45501.53 43885.80 46709.53 36158.16
## 482 483 484 485 486 487 488 489
## 29740.65 31603.90 34291.80 35776.93 36739.65 30401.12 42890.61 49529.16
## 490 491 492 493 494 495 496 497
## 56337.23 51065.90 55883.24 63492.09 67291.10 53592.11 44200.61 42232.01
## 498 499 500 501 502 503 504 505
## 42554.16 43342.74 37847.40 40238.53 45522.80 51185.93 51893.29 52003.50
## 506 507 508 509 510 511 512 513
## 45725.91 47061.53 43332.92 46079.41 45746.19 39482.41 40609.81 39787.64
## 514 515 516 517 518 519 520 521
## 40889.67 43521.11 36384.54 31679.27 55491.18 63625.30 67266.35 60666.99
## 522 523 524 525 526 527 528 529
## 62002.11 75543.51 82497.48 57529.31 52416.07 49121.18 53485.89 52993.83
## 530 531 532 533 534 535 536 537
## 43209.41 48184.95 60804.18 55342.92 58729.48 62704.25 59766.46 54813.42
## 538 539 540 541 542 543 544 545
## 48297.02 46957.30 54868.52 54619.76 47204.07 49420.45 49247.68 49939.24
## 546 547 548 549 550 551 552 553
## 40618.77 32481.17 36808.81 44898.89 44696.52 46396.83 40426.22 49409.75
## 554 555 556 557 558 559 560 561
## 50561.28 40373.86 49883.65 57720.74 57087.76 60673.70 56454.77 68176.83
## 562 563
## 84810.87 74934.28
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8392
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 6.120318 0.5483265 3.23998
## t2* 1726.036188 26.0080576 230.70820
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 2.106346 6.238509 12.66864
## 2 lag_depvar 1401.335790 1734.037187 2161.65413Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Mar 20 00:43:42 2023
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## =-=-=-=-= Iteration 12000 Mon Mar 20 00:44:42 2023
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## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 0.0000 | 5.410333 | 5.629750 | 6.6938684 |
| Comida | 344.5585 | 310.278417 | 314.087500 | 340.5900263 |
| Comunicaciones | 0.0000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 20.2200 | 47.072333 | 38.297667 | 31.7589211 |
| Enceres | 3.7500 | 20.086417 | 17.443792 | 23.5340789 |
| Farmacia | 0.0000 | 1.831667 | 7.913875 | 9.4308947 |
| Gas/Bencina | 35.1500 | 44.325000 | 28.954333 | 25.5869474 |
| Diosi | 13.7950 | 31.180667 | 41.934250 | 38.9333684 |
| donaciones/regalos | 0.0000 | 0.000000 | 7.170083 | 7.2294474 |
| Electrodomésticos/ Mantención casa | 0.0000 | 3.944000 | 30.269500 | 21.8281053 |
| VTR | 10.9950 | 25.156667 | 22.121792 | 20.5862632 |
| Netflix | 4.1600 | 7.151583 | 7.090167 | 7.3013421 |
| Otros | 0.0000 | 3.151083 | 1.575542 | 0.9950789 |
| Total | 432.6285 | 499.588167 | 522.488250 | 534.4683421 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1925, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-04-09 00:04:58 sería de: 35.440 pesos// Percentil 95% más alto proyectado: 38.819,45
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 35562.07 | 35561.61 |
| Lo.80 | 35562.74 | 35562.43 |
| Point.Forecast | 35564.01 | 35564.00 |
| Hi.80 | 37322.24 | 39884.86 |
| Hi.95 | 38359.03 | 42352.99 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.2960 1000.211
## s.e. 0.1424 32.701
##
## sigma^2 = 27313: log likelihood = -318.82
## AIC=643.65 AICc=644.18 BIC=649.32paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.2809 741.3630 8.5540
## s.e. 0.1442 394.4049 12.9803
##
## sigma^2 = 27673: log likelihood = -318.61
## AIC=645.22 AICc=646.13 BIC=652.79forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 729.8856 | 661.0978 | 710.2632 |
| Lo.80 | 847.4742 | 778.4768 | 793.1745 |
| Point.Forecast | 1069.6043 | 1000.2110 | 977.1022 |
| Hi.80 | 1291.7345 | 1221.9451 | 1273.8932 |
| Hi.95 | 1409.3231 | 1339.3241 | 1465.9060 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.11 scales_1.2.1 ggiraph_0.8.7
## [7] tidytext_0.4.1 DT_0.27 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.1 xts_0.13.0
## [13] forecast_8.21 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.0 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.2 forcats_1.0.0
## [22] dplyr_1.1.0 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.2.0 ggplot2_3.4.1 tidyverse_2.0.0
## [28] sjPlot_2.8.13 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.8.0 httr_1.4.5
## [34] readxl_1.4.2 zoo_1.8-11 stringr_1.5.0
## [37] stringi_1.7.12 DataExplorer_0.8.2 data.table_1.14.8
## [40] reshape2_1.4.4 fUnitRoots_4021.80 plyr_1.8.8
## [43] readr_2.1.4
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 igraph_1.4.1 lazyeval_0.2.2
## [7] splines_4.1.2 crosstalk_1.2.0 digest_0.6.31
## [10] htmltools_0.5.4 fansi_1.0.4 ggfortify_0.4.15
## [13] magrittr_2.0.3 tzdb_0.3.0 modelr_0.1.10
## [16] vroom_1.6.1 timechange_0.2.0 anytime_0.3.9
## [19] tseries_0.10-53 colorspace_2.1-0 xfun_0.37
## [22] crayon_1.5.2 jsonlite_1.8.4 lme4_1.1-32
## [25] glue_1.6.2 r2d3_0.2.6 gtable_0.3.2
## [28] emmeans_1.8.5 sjstats_0.18.2 sjmisc_2.8.9
## [31] car_3.1-1 quantmod_0.4.20 abind_1.4-5
## [34] mvtnorm_1.1-3 DBI_1.1.3 ggeffects_1.2.0
## [37] Rcpp_1.0.10 viridisLite_0.4.1 xtable_1.8-4
## [40] performance_0.10.2 bit_4.0.5 datawizard_0.6.5
## [43] htmlwidgets_1.6.1 timeSeries_4021.105 gplots_3.1.3
## [46] ellipsis_0.3.2 spatial_7.3-14 farver_2.1.1
## [49] pkgconfig_2.0.3 nnet_7.3-16 sass_0.4.5
## [52] dbplyr_2.3.1 janitor_2.2.0 utf8_1.2.3
## [55] labeling_0.4.2 tidyselect_1.2.0 rlang_1.1.0
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.7 cli_3.6.0 generics_0.1.3
## [64] sjlabelled_1.2.0 broom_1.0.4 evaluate_0.20
## [67] fastmap_1.1.1 yaml_2.3.7 knitr_1.42
## [70] bit64_4.0.5 caTools_1.18.2 forge_0.2.0
## [73] nlme_3.1-153 slam_0.1-50 xml2_1.3.3
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.14
## [79] curl_5.0.0 bslib_0.4.2 highr_0.10
## [82] fBasics_4022.94 Matrix_1.5-3 its.analysis_1.6.0
## [85] nloptr_2.0.3 urca_1.3-3 vctrs_0.6.0
## [88] pillar_1.8.1 lifecycle_1.0.3 networkD3_0.4
## [91] lmtest_0.9-40 jquerylib_0.1.4 estimability_1.4.1
## [94] bitops_1.0-7 insight_0.19.1 R6_2.5.1
## [97] KernSmooth_2.23-20 janeaustenr_1.0.0 codetools_0.2-18
## [100] gtools_3.9.4 boot_1.3-28 MASS_7.3-54
## [103] assertthat_0.2.1 rprojroot_2.0.3 withr_2.5.0
## [106] fracdiff_1.5-2 bayestestR_0.13.0 parallel_4.1.2
## [109] hms_1.1.2 quadprog_1.5-8 timeDate_4022.108
## [112] minqa_1.2.5 snakecase_0.11.0 rmarkdown_2.20
## [115] carData_3.0-5 TTR_0.24.3 base64enc_0.1-3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))